Correlation and Linear Regression

1.Correlation: Correlation Does Not Mean Causation

One of the major misconceptions about correlation is that a relationship between two variables means causation; that is, one variable causes changes in the other variable. There is a particular tendency to make this causal error, when the two variables only SEEM seem to be related to each other.Keep in mind that one variable is the INDEPENDENT variable and the other is the DEPENDENT variable, meaning that it appears to change as the independent variable does.

What is one instance where you have seen correlation misinterpreted as causation?Please describe.

2. Linear Regression

Linear regression is used to predict the value of the DEPENDENT variable from the INDEPENDENT variable. Since it is based on correlation, it cannot prove causation. In addition, the strength of the relationship between the two variables affects the ability to predict one variable from the other variable; that is, the stronger the relationship between the two variables, the better the ability to do prediction. BUT this predictive ability only extends to the limits of the data.Going outside those limits is referred to as EXTRAPOLATION and is not reliable - don't do it.

What is one instance where you think linear regression would be useful to you in your workplace or chosen major? Please describe including why and how it would be used.